Data Tensei

Today we will learn about "How to Install Ubuntu Server on Oracle VirtualBox" First, you must Install Oracle VirtualBox, its easy, you can download in here . Go to Ubuntu Server and download the ISO file. Create a New Virtual Machine - Open VirtualBox and click New. - Enter a name for your virtual machine and specify the directory for the ISO file. Set Username and Password - During the setup, create a username and password for your Ubuntu server. Adjust Memory (RAM) and Processor - Allocate an appropriate amount of RAM and CPU cores based on your system's resources. Configure Storage - Choose VDI (VirtualBox Disk Image) for the hard disk file type. - Adjust the size as needed and click Finish. Attach the ISO File - Then turn it off after the initial setup. - Go to Settings > Storage. - Under Controller: IDE, click the CD/DVD icon. - Next to Optical Drive, click the CD/DVD icon and select your downloaded ISO file. Start the Virtual Machine - Start the machine and foll...

If you’ve just discovered data analytics and are eager to learn more, whether you’re considering a career in the field or are already a seasoned analyst looking to dive into specialized topics, there’s a wealth of great blogs out there to help you on your journey. From beginner’s guides to student support and career development tips, these resources are designed to help you expand your expertise at any stage. The demand for data analysts is at an all-time high, but finding good, unbiased online resources dedicated to data professionals can still be challenging. That’s why we turned to our team of data analysts to share their favorite sites. From their insights, we’ve created a must-follow list of forums, blogs, and resource centers that every data enthusiast should know about. As the field of data analytics continues to evolve rapidly with advances in AI and machine learning, staying up-to-date with the latest industry trends is crucial. Fortunately, there are plenty of valuable online...

If you're looking to dive into the world of data engineering or just want to stay updated with the latest trends and tips, these blogs and websites are fantastic resources. They offer a mix of practical advice, real-world insights, and community interaction, making them perfect for both newcomers and experienced professionals.

In today's data-driven world, two key roles often stand out: Machine Learning Engineers and Data Engineers . Although both are crucial, they focus on different aspects of data management and utilization. Understanding their distinct roles can help individuals choose a career path and organizations build effective teams.

As our world relies more on data, the need for skilled professionals in Machine Learning (ML) and Big Data is growing. Two popular career paths in this area are Machine Learning Engineer and Data Analyst . Both roles involve working with data, but they differ in their focus, responsibilities, skills, education, tools, industries, job outlooks, and how to get started. This article will compare these two roles to help you decide which career might be right for you.

Data Analysts and Data Engineers play key roles in the world of data science, each with unique responsibilities and skills. In today’s data-driven business environment, both roles are in high demand. Data Engineers are experts in creating and managing data pipelines. They focus on the technical aspects of data collection, processing, and storage, requiring strong programming and data architecture skills. Data Analysts, on the other hand, use their expertise to interpret data and extract meaningful insights. They rely on tools for trend analysis and visualization to help businesses make informed decisions.